Title :
Blind source separation by sensor-signal identity mapping by auto-encoder with hidden-layer pruning
Author_Institution :
Graduate Sch. of Life Sci. & Syst. Eng., Kyushu Inst. of Technol., Iizuka, Japan
fDate :
6/24/1905 12:00:00 AM
Abstract :
A new non-information-theoretic approach is described for the blind source separation (BSS) problem. It is based on an auto-encoder neural network which incorporates a pruning algorithm. Hidden units are nonlinear, and ones that survive the pruning become the source extractors. As such, no assumption is needed for the number of sources. Simulation results show that the auto-encoder can make BSS for a broad class of source-signal mixtures without changing the nonlinear activation function of the hidden units
Keywords :
deconvolution; encoding; feedforward neural nets; multilayer perceptrons; neural net architecture; signal sources; transfer functions; auto-associative neural network; auto-encoder neural network; blind source separation; hidden-layer pruning algorithm; noninformation-theoretic approach; nonlinear activation function; nonlinear hidden units; sensor-signal identity mapping; simulation; source extractors; source-signal mixtures; Blind source separation; Data compression; Decoding; Independent component analysis; Maximum likelihood estimation; Modeling; Neural networks; Principal component analysis; Source separation; Systems engineering and theory;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007683
